Predictive Maintenance using Machine Learning Based Classification Models

Machine learning facilitates predictive maintenance due to the advantages it holds over traditional methods of maintaining semi-conductor devices such as preventive and breakdown maintenance. Several predictive models using machine learning on the Semiconductor Manufacturing process dataset (SECOM)...

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Vydáno v:	IOP conference series. Materials Science and Engineering Ročník 954; číslo 1; s. 12001 - 12009
Hlavní autoři:	Chazhoor, Anisha, Mounika, Y, Vergin Raja Sarobin, M, Sanjana, M V, Yasashvini, R
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	IOP Publishing 01.10.2020
ISSN:	1757-8981, 1757-899X
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Machine learning facilitates predictive maintenance due to the advantages it holds over traditional methods of maintaining semi-conductor devices such as preventive and breakdown maintenance. Several predictive models using machine learning on the Semiconductor Manufacturing process dataset (SECOM) will be applied in this paper. The dataset contains the information related to semiconductor manufacturing process, with the attributes corresponding to signals collected from semiconductor devices. Due to the high-dimensionality of the data and class imbalance problem in the SECOM dataset, it poses several challenges related to data pre-processing, which is an essential step incorporated in this work while applying various machine learning models. Comparison and analysis of various predictive machine learning classification models were carried out based on the performance metrics like, accuracy and Receiver Operating Characteristic (ROC) curve.
ISSN:	1757-8981 1757-899X
DOI:	10.1088/1757-899X/954/1/012001